pandas

Auto-generated from pandas-dev/pandas by Mutable.ai Auto WikiRevise

The pandas library is a powerful open-source data analysis and manipulation tool for Python. It provides high-performance, easy-to-use data structures and data analysis tools for working with structured (tabular, multidimensional, potentially heterogeneous) and time series data. The library is built on top of NumPy and provides a wide range of functionality for reading, processing, and analyzing data from various sources.

The core of the pandas library is the DataFrame and Series classes, which provide labeled, tabular data structures similar to R's data.frame objects. These data structures allow for efficient storage, manipulation, and analysis of data, with support for a wide range of data types, including numeric, string, datetime, and categorical data.

The …/core directory contains the implementation of the core data structures and their associated functionality. This includes the Index and ExtensionArray classes, which provide the fundamental building blocks for indexing and storing data in the DataFrame and Series objects. The …/ops and …/window directories handle the implementation of various operations on these data structures, such as arithmetic, comparison, and window-based calculations.

The …/io directory provides a wide range of functionality for reading and writing data in various formats, including CSV, Excel, JSON, and SQL databases. This allows users to easily integrate pandas with their existing data sources and workflows.

The …/tseries directory contains the functionality for working with time series and date/time data in the pandas library. This includes the implementation of time series offsets, frequency inference, and holiday calendars, which are essential for handling and analyzing temporal data.

The …/arrays directory defines the various ExtensionArray classes that provide efficient and flexible storage and manipulation of different data types within pandas. This allows for the integration of custom data types and the extension of the library's capabilities.

The …/tests directory contains a comprehensive suite of unit tests that verify the functionality and correctness of the pandas library. These tests cover a wide range of scenarios, including data structures, I/O operations, indexing, grouping, arithmetic operations, and more. The tests help ensure the reliability and robustness of the pandas library.

Overall, the pandas library is a powerful and flexible tool for data analysis and manipulation in Python. Its core data structures, I/O functionality, time series handling, and extensibility make it a popular choice for a wide range of data-driven applications.

References: pandas/core

The core data structures provided by the pandas library are the DataFrame and Series classes. These classes are the fundamental building blocks for working with tabular and one-dimensional data in the pandas library.

References: pandas/core/indexes, pandas/core/arrays

The core data structures provided by the pandas library are the DataFrame and Series classes. These classes provide a powerful and flexible way to work with structured, tabular data.

References: pandas/core/indexes

The pandas library provides a rich set of functionality for indexing and labeling data in the core data structures, including the Index, MultiIndex, CategoricalIndex, IntervalIndex, RangeIndex, TimedeltaIndex, PeriodIndex, and DatetimeIndex classes.

References: pandas/core/ops, pandas/core/window, pandas/core/groupby

The core functionality for performing data manipulation, transformation, and computation operations on the Pandas data structures is primarily provided by the …/ops directory. This directory contains several Python files that handle different aspects of these operations, including handling missing values, dispatching to ExtensionArrays, and generating docstrings for the operations.

References: pandas/core/dtypes, pandas/core/internals, pandas/core/util

The pandas.core.dtypes directory contains a collection of modules that provide functionality for working with data types in the Pandas library. This includes functions and classes for type checking, type conversion, type promotion, and handling missing values.

References: pandas/io

The Pandas library provides a wide range of functionality for reading and writing data in various formats, including CSV, Excel, JSON, and SQL databases. The key components that handle these I/O operations are located in the …/io directory.

References: pandas/io/parsers, pandas/io/excel, pandas/io/json

The core functionality for reading data into Pandas DataFrames is provided by the TextFileReader class and the read_csv(), read_table(), and read_fwf() functions in the …/readers.py module.

References: pandas/io/formats, pandas/io/excel, pandas/io/json

The core functionality for writing data to various file formats in the Pandas library is provided by the …/formats directory. This directory contains several key components:

References: pandas/io/clipboard

The pandas.io.clipboard module provides cross-platform functionality for copying and pasting plain text to and from the system clipboard. It supports various clipboard mechanisms, including:

References: pandas

The Pandas library provides functionality for reading and writing data in specialized formats, such as Feather, ORC, Parquet, and SPSS. This functionality is primarily implemented in the following files and directories:

References: pandas/core/ops, pandas/core/window, pandas/core/groupby

The core functionality for performing data manipulation, transformation, and computation operations on the core Pandas data structures is provided in several key modules and classes:

References: pandas/core/window

The …/window directory provides the core functionality for performing various window-based calculations in the Pandas library. The main components in this directory are:

References: pandas/core/ops

The core functionality for performing arithmetic, comparison, and logical operations on Pandas objects is provided in the …/ops directory. This subsection explains the key components and design choices in this part of the Pandas library.

References: pandas/core/groupby

The DataFrameGroupBy and SeriesGroupBy classes are the main entry points for grouping and aggregating data in the Pandas library. These classes provide a rich set of functionality for performing various groupby operations on DataFrame and Series objects, respectively.

References: pandas/arrays, pandas/tests/extension

The extension array functionality in pandas allows for the integration of custom data types and extension arrays. This is achieved through the ExtensionArray and ExtensionDtype classes, which provide the necessary infrastructure for working with these custom data structures.

References: pandas/arrays, pandas/tests/extension

The extension array functionality in pandas is provided by the …/arrays directory. This directory contains the main implementation of various extension array types, including:

References: pandas/tests/extension/base

The …/base directory contains a set of base test classes that are used to validate the behavior of extension arrays and data types in the Pandas library. These test classes cover a wide range of functionality, including constructors, data type handling, indexing, grouping, arithmetic operations, and more. The tests ensure that extension arrays and data types adhere to the expected Pandas interface and behavior.

References: pandas/tests/extension/list, pandas/tests/extension/json, pandas/tests/extension/decimal

The …/list directory contains the implementation and tests for a custom list-based extension array in the Pandas library. The ListArray class is a subclass of ExtensionArray and provides functionality for storing and manipulating data as lists within Pandas data structures. The ListDtype class, a subclass of ExtensionDtype, defines the data type information and metadata for the ListArray.

References: pandas/tests/extension/array_with_attr

The FloatAttrArray class and its associated FloatAttrDtype class provide a custom extension array type that can store float values along with an additional attribute. This functionality is implemented in the …/array_with_attr directory.

References: pandas/core/indexes

The Pandas library provides a rich set of functionality for indexing and labeling data in its core data structures, the DataFrame and Series. This is primarily handled through the various index types implemented in the …/indexes directory.

References: pandas

The core data structures provided by the pandas library include the Index class and several specialized index types, such as MultiIndex, CategoricalIndex, IntervalIndex, RangeIndex, TimedeltaIndex, PeriodIndex, and DatetimeIndex. These index types provide the fundamental functionality for indexing and labeling data in Pandas.

References: pandas

The …/indexes directory contains the implementation of various index types, such as Index, MultiIndex, CategoricalIndex, DatetimeIndex, and IntervalIndex. These index types provide the fundamental functionality for indexing and labeling data in Pandas.

References: pandas

The …/indexes directory contains the implementation of various index types, such as Index, MultiIndex, CategoricalIndex, DatetimeIndex, and IntervalIndex. These index types provide the fundamental functionality for indexing and labeling data in Pandas.

References: pandas/core/dtypes, pandas/core/internals, pandas/core/util

The core internal data structures and components that underlie the Pandas data structures are managed by the BlockManager and SingleBlockManager classes, which are defined in the …/managers.py file.

References: pandas/core/util/hashing.py, pandas/core/util/numba_.py

The hashing.py file in the Pandas library provides functionality for generating deterministic hashes for Pandas objects, such as Index, Series, and DataFrame. The main purpose of this is to allow for efficient caching, data analysis, and performance optimization by providing a way to uniquely identify Pandas data structures.

References: pandas/core/dtypes

The pandas.core.dtypes directory contains a collection of modules that provide functionality for working with data types in the Pandas library. This includes functions and classes for type checking, type conversion, type promotion, and handling missing values.

References: pandas/core/internals

The core internal data structures and components that underlie the Pandas data structures include the Block class, BlockManager, and various utility functions for managing and manipulating these components.

References: pandas/core/strings

The pandas library provides a comprehensive set of functionality for working with string and text data through the StringMethods accessor. This accessor is the main entry point for applying string operations to Pandas Series objects.

References: pandas/tseries

The Pandas library provides a rich set of tools and functionality for working with time series and date/time data. The key components that enable this functionality are located in the …/tseries directory.

References: pandas

The pandas.tseries.offsets module defines a variety of time-based offsets that can be used to perform date and time calculations in the Pandas library. These offsets include:

References: pandas

The pandas.tseries.frequencies module provides functionality for inferring the frequency of a time series. The core of this functionality is the infer_freq() function, which uses various heuristics to determine the frequency of a time series based on the input data.

References: pandas

The pandas.tseries.holiday module defines a Holiday class and an AbstractHolidayCalendar class, which can be used to work with holidays and create custom holiday calendars, such as the USFederalHolidayCalendar.

References: pandas

The pandas.tseries.api module exposes some of the key time series-related functions and classes in the Pandas library.